
'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


lowercase       :				Optional[int] =   logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (  __A      )			->      Union[str, Any]:




	if isinstance(__A     ,							(list, tuple)      ) and isinstance(videos[0]     ,							(list, tuple)      ) and is_valid_image(videos[0][0]      ):
		return videos

	elif isinstance(__A     ,							(list, tuple)      ) and is_valid_image(videos[0]      ):
		return [videos]

	elif is_valid_image(__A      ):
		return [[videos]]

	raise ValueError(F'Could not make batched video from {videos}'      )





class 		__UpperCAmelCase (   _UpperCAmelCase		):
							__lowercase 						=			["""pixel_values"""]


							def __init__(			self       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = PILImageResampling.BILINEAR       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = 1 / 2_55       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_    )
								_snake_case  		=						size if size is not None else {'shortest_edge': 2_24}
								_snake_case  		=						get_size_dict(lowerCAmelCase_       ,  default_to_square=lowerCAmelCase_    )
								_snake_case  		=						crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								_snake_case  		=						get_size_dict(lowerCAmelCase_       ,  param_name='crop_size'    )

								_snake_case  		=						do_resize
								_snake_case  		=						size
								_snake_case  		=						do_center_crop
								_snake_case  		=						crop_size
								_snake_case  		=						resample
								_snake_case  		=						do_rescale
								_snake_case  		=						rescale_factor
								_snake_case  		=						do_normalize
								_snake_case  		=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_snake_case  		=						image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = PILImageResampling.BILINEAR       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):





								"""simple docstring"""






								_snake_case  		=						get_size_dict(lowerCAmelCase_       ,  default_to_square=lowerCAmelCase_    )
								if "shortest_edge" in size:
									_snake_case  		=						get_resize_output_image_size(lowerCAmelCase_       ,  size['shortest_edge']       ,  default_to_square=lowerCAmelCase_    )
								elif "height" in size and "width" in size:
									_snake_case  		=						(size['height'], size['width'])
								else:
									raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'    )
								return resize(lowerCAmelCase_       ,  size=lowerCAmelCase_       ,  resample=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):





								"""simple docstring"""






								_snake_case  		=						get_size_dict(lowerCAmelCase_    )
								if "height" not in size or "width" not in size:
									raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'    )
								return center_crop(lowerCAmelCase_       ,  size=(size['height'], size['width'])       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):





								"""simple docstring"""






								return rescale(lowerCAmelCase_       ,  scale=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):





								"""simple docstring"""






								return normalize(lowerCAmelCase_       ,  mean=lowerCAmelCase_       ,  std=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = ChannelDimension.FIRST       ,  ):





								"""simple docstring"""






								if do_resize and size is None or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.'    )

								if do_center_crop and crop_size is None:
									raise ValueError('Crop size must be specified if do_center_crop is True.'    )

								if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

								# All transformations expect numpy arrays.
								_snake_case  		=						to_numpy_array(lowerCAmelCase_    )

								if do_resize:
									_snake_case  		=						self.resize(image=lowerCAmelCase_       ,  size=lowerCAmelCase_       ,  resample=lowerCAmelCase_    )

								if do_center_crop:
									_snake_case  		=						self.center_crop(lowerCAmelCase_       ,  size=lowerCAmelCase_    )

								if do_rescale:
									_snake_case  		=						self.rescale(image=lowerCAmelCase_       ,  scale=lowerCAmelCase_    )

								if do_normalize:
									_snake_case  		=						self.normalize(image=lowerCAmelCase_       ,  mean=lowerCAmelCase_       ,  std=lowerCAmelCase_    )

								_snake_case  		=						to_channel_dimension_format(lowerCAmelCase_       ,  lowerCAmelCase_    )
								return image




							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = ChannelDimension.FIRST       ,  **lowerCAmelCase_       ,  ):





								"""simple docstring"""






								_snake_case  		=						do_resize if do_resize is not None else self.do_resize
								_snake_case  		=						resample if resample is not None else self.resample
								_snake_case  		=						do_center_crop if do_center_crop is not None else self.do_center_crop
								_snake_case  		=						do_rescale if do_rescale is not None else self.do_rescale
								_snake_case  		=						rescale_factor if rescale_factor is not None else self.rescale_factor
								_snake_case  		=						do_normalize if do_normalize is not None else self.do_normalize
								_snake_case  		=						image_mean if image_mean is not None else self.image_mean
								_snake_case  		=						image_std if image_std is not None else self.image_std

								_snake_case  		=						size if size is not None else self.size
								_snake_case  		=						get_size_dict(lowerCAmelCase_       ,  default_to_square=lowerCAmelCase_    )
								_snake_case  		=						crop_size if crop_size is not None else self.crop_size
								_snake_case  		=						get_size_dict(lowerCAmelCase_       ,  param_name='crop_size'    )

								if not valid_images(lowerCAmelCase_    ):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.'    )

								_snake_case  		=						make_batched(lowerCAmelCase_    )

								_snake_case  		=						[
								    [
								        self._preprocess_image(
								            image=lowerCAmelCase_       ,  do_resize=lowerCAmelCase_       ,  size=lowerCAmelCase_       ,  resample=lowerCAmelCase_       ,  do_center_crop=lowerCAmelCase_       ,  crop_size=lowerCAmelCase_       ,  do_rescale=lowerCAmelCase_       ,  rescale_factor=lowerCAmelCase_       ,  do_normalize=lowerCAmelCase_       ,  image_mean=lowerCAmelCase_       ,  image_std=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  )
								        for img in video
								    ]
								    for video in videos
								]

								_snake_case  		=						{'pixel_values': videos}
								return BatchFeature(data=lowerCAmelCase_       ,  tensor_type=lowerCAmelCase_    )








"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






